Theoretical and Applied Climatology

, Volume 114, Issue 3–4, pp 511–529

Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM

Effects on extreme values and climate change signal
Original Paper

Abstract

A new parametric bias correction method for precipitation with an extension for extreme values is compared to an empirical and an existing parametric method. The bias corrections are applied to the regional climate model COSMO-CLM (consortium for small-scale modelling – climate limited area modelling) with a resolution of 4.5 km for the time periods 1991–2000 and 2091–2100. In addition to a comparison in a cross-validation framework, a focus is laid on the investigation of extreme value correction and the effect of the bias correction on the climate change signal. According to the statistical methods used in this study, it was found that the empirical method outperforms both parametric alternatives. However, due to the limited length of the available time series, some outliers occurred, and all methods had problems correcting extreme values. The climate change signal is moderately influenced by all three methods, and the power of climate change detection is reduced. The largest effect was found for the number of dry days and the mean daily intensity, which are considerably altered after correction.

References

  1. Anagnostopoulou C, Tolika K (2012) Extreme precipitation in Europe: statistical threshold selection based on climatological criteria. Theor Appl Climatol 107:479–489. doi:10.1007/s00704-011-0487-8 CrossRefGoogle Scholar
  2. Bachner S, Kapala A, Simmer C (2008) Evaluation of daily precipitation characteristics in the CLM and their sensitivity to parameterizations. Meteorol Z 17(4):407–419CrossRefGoogle Scholar
  3. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc, Ser B (Methodol) 57:280–300Google Scholar
  4. Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188CrossRefGoogle Scholar
  5. Boé J, Terray L, Habets F, Martin E (2007) Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. Int J Climatol 27:1643–1655. doi:10.1002/joc.1602 CrossRefGoogle Scholar
  6. Casper MC, Grigoryan G, Gronz O, Gutjahr O, Heinemann G, Ley R, Rock A (2012) Analysis of projected hydrological behavior of catchments based on signature indices. Hydrol Earth Syst Sci 16:409–421. doi:10.5194/hess-16-409-2012 CrossRefGoogle Scholar
  7. Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, Inc., Hillsdale, p 590Google Scholar
  8. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, Berlin, p 224Google Scholar
  9. Déqué M (2007) Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statistical correction according to observed values. Global Planet Change 57:16–26. doi:10.1016/j.gloplacha.2006.11.030 CrossRefGoogle Scholar
  10. Fowler HJ, Ekström M (2009) Multi-model ensemble estimates of climate change impacts on UK seasonal precipitation extremes. Int J Climatol 29:385–416. doi:10.1002/joc.1827 CrossRefGoogle Scholar
  11. Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578. doi:10.1002/joc.1556 CrossRefGoogle Scholar
  12. Frei C, Christensen JH, Déqué M, Jacob D, Jones RG, Vidale PL (2003) Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. J Geophys Res 108(D3):4142. doi:10.1029/2002JD002287 CrossRefGoogle Scholar
  13. Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical note: downscaling RCM precipitation to the station scale using statistical transformations—a comparison of methods. Hydrol Earth Syst Sci 16:3383–3390CrossRefGoogle Scholar
  14. Graham LP, Hagemann S, Jaun S, Beniston M (2007) On interpreting hydrological change from regional climate models. Clim Change 81:97–122. doi:10.1007/s10584-006-9217-0 CrossRefGoogle Scholar
  15. Haddeland I, Heinke J, Voß F, Eisner S, Chen C, Hagemann S, Ludwig F (2012) Effects of climate model radiation, humidity and wind estimates on hydrological simulations. Hydrol Earth Syst Sci 16:305–318. doi:10.5194/hessd-8-7919-2011 CrossRefGoogle Scholar
  16. Hagemann S, Chen C, Haerter JO, Heinke J, Gerten D, Piani C (2011) Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J Hydrometeorol 12:556–578. doi:10.1175/2011JHM1336.1 CrossRefGoogle Scholar
  17. Hagemann S, Machenhauer B, Jones R, Christensen OB, Déqué M, Jacob D, Vidale PL (2004) Evaluation of water and energy budgets in regional climate models applied over Europe. Clim Dyn 23:547–567. doi:10.1007/s00382-004-0444-7 CrossRefGoogle Scholar
  18. Hansen JW, Challinor A, Ines A, Wheeler T, Moron V (2006) Translating climate forecasts into agricultural terms: advances and challenges. Clim Res 33:27–41. doi:10.3354/cr033027 CrossRefGoogle Scholar
  19. Hay LE, Wilby RL, Leavesley GH (2000) A comparison of delta change and downscaled GCM scenarios for three mounfainous basins in the United States. J Am Water Resour Assoc 36:387–397. doi:10.1111/j.1752-1688.2000.tb04276.x CrossRefGoogle Scholar
  20. Hohenegger C, Brockhaus P, Schär C (2008) Towards climate simulations at cloud-resolving scales. Meteorol Z 17:383–394. doi:10.1127/0941-2948/2008/0303 CrossRefGoogle Scholar
  21. Hollweg HD, Böhm U, Fast I, Hennemuth B, Keuler K, Keup-Thiel E, Lautenschlager M, Legutke S, Radtke K, Rockel B, Schubert M, Will A, Woldt M, Wunram C (2008) Ensemble simulations over Europe with the regional climate model CLM forced with IPCC AR4 global scenarios. Max-Planck-Institut für Meteorologie Group: Modelle & Daten, Tech ReportGoogle Scholar
  22. Katz RW, Brown BG (1992) Extreme events in a changing climate: variability is more important than averages. Clim Change 21:289–302. doi:10.1007/BF00139728 CrossRefGoogle Scholar
  23. Knote C, Heinemann G, Rockel B (2010) Changes in weather extremes: assessment of return values using high resolution climate simulations at convection-resolving scale. Meteorol Z 19:11–23. doi:10.1127/0941-2948/2010/0424 CrossRefGoogle Scholar
  24. Livezey RE, Chen WY (1983) Statistical field significance and its determination by Monte Carlo techniques. Mon Weather Rev 111:46–59. doi:10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2 CrossRefGoogle Scholar
  25. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change. Recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(RG3003):1–34. doi:10.1029/2009RG000314 Google Scholar
  26. Michelangeli P-A, Vrac M, Loukos H (2009) Probabilistic downscaling approaches: application to wind cumulative distribution function. Geophys Res Lett 36:L11708. doi:10.1029/2009GL038401 CrossRefGoogle Scholar
  27. Panofsky HW, Brier GW (1968) Some applications of statistics to meteorology. Earth and Mineral Sciences Continuing Education, College of Earth and Mineral Sciences, Pennsylvania, p 224Google Scholar
  28. Piani C, Haerter JO, Coppola E (2010a) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. doi:10.1007/s00704-009-0134-9 CrossRefGoogle Scholar
  29. Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO (2010b) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395:199–215. doi:10.1016/j.jhydrol.2010.10.024 CrossRefGoogle Scholar
  30. Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM). Meteorol Z 17(4):347–348. doi:10.1127/0941-2948/2008/0309 CrossRefGoogle Scholar
  31. Sennikovs J, Bethers U (2009) Statistical downscaling method of regional climate model results for hydrological modelling. In: 18th world IMACS/MODSIM congressGoogle Scholar
  32. Sharma D, Gupta D, Babel MS (2007) Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand. Hydrol Earth Syst Sci 11:1373–1390. doi:10.5194/hess-11-1373-2007 CrossRefGoogle Scholar
  33. Steppeler J, Doms G, Schättler U, Bitzer HW, Gassmann A, Damrath U, Gregoric G (2003) Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorol Atmos Phys 82:75–96CrossRefGoogle Scholar
  34. Themeßl MJ, Gobiet A, Heinrich G (2012) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31:1530–1544. doi:10.1007/s10584-011-0224-4 CrossRefGoogle Scholar
  35. Themeßl MJ, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Clim Change 112:449–468. doi:10.1002/joc.2168 CrossRefGoogle Scholar
  36. Trenberth KE, Dai A, Rasmussen RM, Parsons DB (2003) The changing character of precipitation. Bull Am Meteorol Soc 84:1205–1217. doi:10.1175/BAMS-84-9-1205 CrossRefGoogle Scholar
  37. van der Linden P, Mitchell JFB (eds) (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UKGoogle Scholar
  38. van Roosmalen L, Sonnenborg TO, Jensen KH, Christense JH (2011) Comparison of hydrological simulations of climate change using perturbation of observations and distribution-based scaling. Vadose Zone J 10:136–150. doi:10.2136/vzj2010.0112 CrossRefGoogle Scholar
  39. Ventura V, Paciorek C, Risbey JS (2004) Controlling the proportion of falsely rejected hypotheses when conducting multiple tests with climatological data. J Climate 17:4343–4356. doi:10.1175/3199.1 CrossRefGoogle Scholar
  40. Vlc̆ek O, Radan H (2009) Is daily precipitation gamma-distributed?: adverse effects of an incorrect use of the Kolmogorov–Smirnov test. Atmos Res 93:759–766. doi:16/j.atmosres.2009.03.005 CrossRefGoogle Scholar
  41. Wehner M (2010) Sources of uncertainty in the extreme value statistics of climate data. Extremes 13:205–217. doi:10.1007/s10687-010-0105-7 CrossRefGoogle Scholar
  42. Wilks DS (2011) Statistical methods in the atmospheric sciences, 3rd edn. Academic, Burlington, p 704. ISBN: 0123850223Google Scholar
  43. Wilks DS (2006) On field significance and the false discovery rate. J Appl Meteorol Climatol 45:1181–1189. doi:10.1175/JAM2404.1 CrossRefGoogle Scholar
  44. Wood A, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate outputs. Clim Change 62:189–216. doi:10.1023/B:CLIM.000001368599609.9e CrossRefGoogle Scholar
  45. Yang W, Andréasson J, Graham LP, Olsson J, Rosberg J, Wetterhall F (2010) Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies. Hydrol Res 41:211–229. doi:10.2166/nh.2010.004 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.University of TrierTrierGermany

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